from utils import print_time_report, print_results, plot_results
print_time_report()
| hour | min | sec | |
|---|---|---|---|
| algo | |||
| KNeighborsClassifier | 0 | 5 | 11 |
| daal4py_KNeighborsClassifier | 0 | 1 | 9 |
| KNeighborsClassifier_kd_tree | 0 | 1 | 16 |
| daal4py_KNeighborsClassifier_kd_tree | 0 | 0 | 46 |
| KMeans | 0 | 7 | 32 |
| daal4py_KMeans | 0 | 1 | 58 |
| total | 0 | 17 | 55 |
print_results(algo="KNeighborsClassifier", versus_lib="daal4py")
plot_results(algo="KNeighborsClassifier", versus_lib="daal4py", group_by_cols=["algorithm", "n_neighbors", "function"], split_hist_by=["n_jobs"])
print_results(algo="KNeighborsClassifier_kd_tree", versus_lib="daal4py")
plot_results(algo="KNeighborsClassifier_kd_tree", versus_lib="daal4py", group_by_cols=["algorithm", "n_neighbors", "function"], split_hist_by=["n_jobs"])
from utils import _make_dataset
data = _make_dataset('KMeans', 'daal4py', compare_cols=["n_iter"])
data[data["function"] == "fit"].sort_values(["n_iter_sklearn", "n_iter_daal4py"])
| estimator | lib | function | n_samples | n_features | init | max_iter | n_clusters | n_init | tol | adjusted_rand_score | mean_sklearn | stdev_sklearn | n_iter_sklearn | mean_daal4py | stdev_daal4py | n_iter_daal4py | speedup | stdev_speedup | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 18 | KMeans | sklearn | fit | 10000 | 100 | k-means++ | 30 | 3 | 1 | 0.0 | NaN | 0.4371 | 0.2018 | 24.0 | 0.1011 | 0.0142 | 30.0 | 4.32 | 14.21 |
| 0 | KMeans | sklearn | fit | 10000 | 2 | k-means++ | 30 | 3 | 1 | 0.0 | NaN | 0.0862 | 0.0478 | 30.0 | 0.0126 | 0.0236 | 30.0 | 6.84 | 2.03 |
| 9 | KMeans | sklearn | fit | 10000 | 2 | random | 30 | 3 | 1 | 0.0 | NaN | 0.1038 | 0.0440 | 30.0 | 0.0105 | 0.0146 | 30.0 | 9.89 | 3.01 |
| 27 | KMeans | sklearn | fit | 10000 | 100 | random | 30 | 3 | 1 | 0.0 | NaN | 0.0226 | 0.0271 | 30.0 | 0.0090 | 0.0142 | 30.0 | 2.51 | 1.91 |
| 36 | KMeans | sklearn | fit | 1000000 | 2 | k-means++ | 30 | 3 | 1 | 0.0 | NaN | 0.0305 | 0.0269 | 30.0 | 0.0087 | 0.0137 | 30.0 | 3.51 | 1.96 |
| 45 | KMeans | sklearn | fit | 1000000 | 2 | random | 30 | 3 | 1 | 0.0 | NaN | 0.2784 | 0.0624 | 30.0 | 0.0459 | 0.0143 | 30.0 | 6.07 | 4.36 |
| 54 | KMeans | sklearn | fit | 1000000 | 100 | k-means++ | 30 | 3 | 1 | 0.0 | NaN | 0.1417 | 0.0276 | 30.0 | 0.0190 | 0.0146 | 30.0 | 7.46 | 1.89 |
| 63 | KMeans | sklearn | fit | 1000000 | 100 | random | 30 | 3 | 1 | 0.0 | NaN | 0.1836 | 0.0334 | 30.0 | 0.0285 | 0.0149 | 30.0 | 6.44 | 2.24 |
| 3 | KMeans | sklearn | fit | 10000 | 2 | k-means++ | 30 | 10 | 1 | 0.0 | NaN | 0.0055 | 0.0156 | NaN | 0.0043 | 0.0126 | NaN | 1.28 | 1.24 |
| 6 | KMeans | sklearn | fit | 10000 | 2 | k-means++ | 30 | 300 | 1 | 0.0 | NaN | 0.0052 | 0.0148 | NaN | 0.0045 | 0.0131 | NaN | 1.16 | 1.13 |
| 12 | KMeans | sklearn | fit | 10000 | 2 | random | 30 | 10 | 1 | 0.0 | NaN | 0.0055 | 0.0156 | NaN | 0.0043 | 0.0125 | NaN | 1.28 | 1.25 |
| 15 | KMeans | sklearn | fit | 10000 | 2 | random | 30 | 300 | 1 | 0.0 | NaN | 0.0051 | 0.0145 | NaN | 0.0044 | 0.0128 | NaN | 1.16 | 1.13 |
| 21 | KMeans | sklearn | fit | 10000 | 100 | k-means++ | 30 | 10 | 1 | 0.0 | NaN | 0.0060 | 0.0160 | NaN | 0.0048 | 0.0125 | NaN | 1.25 | 1.28 |
| 24 | KMeans | sklearn | fit | 10000 | 100 | k-means++ | 30 | 300 | 1 | 0.0 | NaN | 0.0053 | 0.0151 | NaN | 0.0045 | 0.0131 | NaN | 1.18 | 1.15 |
| 30 | KMeans | sklearn | fit | 10000 | 100 | random | 30 | 10 | 1 | 0.0 | NaN | 0.0054 | 0.0153 | NaN | 0.0044 | 0.0126 | NaN | 1.23 | 1.21 |
| 33 | KMeans | sklearn | fit | 10000 | 100 | random | 30 | 300 | 1 | 0.0 | NaN | 0.0051 | 0.0145 | NaN | 0.0044 | 0.0128 | NaN | 1.16 | 1.13 |
| 39 | KMeans | sklearn | fit | 1000000 | 2 | k-means++ | 30 | 10 | 1 | 0.0 | NaN | 0.0055 | 0.0158 | NaN | 0.0043 | 0.0125 | NaN | 1.28 | 1.26 |
| 42 | KMeans | sklearn | fit | 1000000 | 2 | k-means++ | 30 | 300 | 1 | 0.0 | NaN | 0.0051 | 0.0147 | NaN | 0.0046 | 0.0133 | NaN | 1.11 | 1.11 |
| 48 | KMeans | sklearn | fit | 1000000 | 2 | random | 30 | 10 | 1 | 0.0 | NaN | 0.0058 | 0.0153 | NaN | 0.0048 | 0.0125 | NaN | 1.21 | 1.22 |
| 51 | KMeans | sklearn | fit | 1000000 | 2 | random | 30 | 300 | 1 | 0.0 | NaN | 0.0052 | 0.0148 | NaN | 0.0045 | 0.0131 | NaN | 1.16 | 1.13 |
| 57 | KMeans | sklearn | fit | 1000000 | 100 | k-means++ | 30 | 10 | 1 | 0.0 | NaN | 0.0193 | 0.0563 | NaN | 0.0045 | 0.0128 | NaN | 4.29 | 4.40 |
| 60 | KMeans | sklearn | fit | 1000000 | 100 | k-means++ | 30 | 300 | 1 | 0.0 | NaN | 0.0052 | 0.0148 | NaN | 0.0045 | 0.0130 | NaN | 1.16 | 1.14 |
| 66 | KMeans | sklearn | fit | 1000000 | 100 | random | 30 | 10 | 1 | 0.0 | NaN | 0.0056 | 0.0152 | NaN | 0.0046 | 0.0127 | NaN | 1.22 | 1.20 |
| 69 | KMeans | sklearn | fit | 1000000 | 100 | random | 30 | 300 | 1 | 0.0 | NaN | 0.0051 | 0.0145 | NaN | 0.0045 | 0.0129 | NaN | 1.13 | 1.12 |
print_results(algo="KMeans", versus_lib="daal4py", compare_cols=["n_iter"])
plot_results(algo="KMeans", versus_lib="daal4py", group_by_cols=["init", "max_iter", "n_clusters", "n_init", "tol", "function"], compare_cols=["n_iter"])